The Brain Imaging Data Structure (BIDS) is an increasingly adopted standard for organizing scientific data and metadata. It facilitates easier and more straightforward data sharing and reuse. BIDS currently encompasses several biomedical imaging and non-imaging techniques, and as more research groups begin to use it, additional experimental techniques are being incorporated into the standard, allowing diverse experimental methods to be stored within the same cohesive structure. Here, we present an extension for magnetic resonance spectroscopy (MRS) data, termed MRS-BIDS.

Researchers may avoid taking on interdisciplinary projects due to concern that publications outside of their own field would not be rewarded in their home departments. This means that data with an interdisciplinary focus is underprovided. We propose that journal parent publishers facilitate “cross-listed” journal publications where papers can be submitted to and peer-reviewed simultaneously by two journals, in different fields, with joint publication under a single DOI.

Research software has become indispensable in contemporary research and is now viewed as essential infrastructure in many scholarly fields. Encompassing source code, algorithms, scripts, computational workflows, and executables generated during or specifically for research, it plays a critical role in advancing scholarly knowledge. The research software field includes considerable open-source use and links to the broader open science movement. In this context, it has been argued that the well-established FAIR (Findable, Accessible, Interoperable, Reusable) principles for research data should be adapted for research software under the label FAIR4RS. However, the level of uptake of FAIR4RS principles is unclear. To gauge FAIR4RS’s status, international research funders involved in supporting research software (n = 36) were surveyed about their awareness of the concept. The survey reveals much greater familiarity with the more established FAIR principles for data (73% ‘extremely familiar’) than FAIR4RS (33% ‘extremely familiar’). Nevertheless, there is still considerable recognition of the relatively new FAIR4RS concept, a significant achievement for efforts to extend open science policies and practices to encompass research software.

The CARE Principles (Collective Benefit, Authority to Control, Responsibility, Ethics) were developed to ensure ethical stewardship of Indigenous data. However, their adaptability makes them an ideal framework for managing data related to vulnerable populations affected by armed conflicts. This essay explores the application of CARE principles to wartime contexts, with a particular focus on internally displaced persons (IDPs) and civilians living under occupation. These groups face significant risks of data misuse, ranging from privacy violations to targeted repression. By adapting CARE, data governance can prioritize safety, dignity, and empowerment while ensuring that data serves the collective welfare of affected communities. Drawing on examples from Indigenous data governance, open science initiatives, and wartime humanitarian challenges, this essay argues for extending CARE principles beyond their original scope. Such an adaptation highlights CARE’s potential as a universal standard for addressing the ethical complexities of data management in humanitarian crises and conflict-affected environments.

Recent trends within computational and data sciences show an increasing recognition and adoption of computational workflows as tools for productivity and reproducibility that also democratize access to platforms and processing know-how. As digital objects to be shared, discovered, and reused, computational workflows benefit from the FAIR principles, which stand for Findable, Accessible, Interoperable, and Reusable. The Workflows Community Initiative’s FAIR Workflows Working Group (WCI-FW), a global and open community of researchers and developers working with computational workflows across disciplines and domains, has systematically addressed the application of both FAIR data and software principles to computational workflows. We present recommendations with commentary that reflects our discussions and justifies our choices and adaptations. These are offered to workflow users and authors, workflow management system developers, and providers of workflow services as guidelines for adoption and fodder for discussion. The FAIR recommendations for workflows that we propose in this paper will maximize their value as research assets and facilitate their adoption by the wider community.

Scientists are increasingly required by funding agencies, publishers and their institutions to produce and publish data that are Findable, Accessible, Interoperable and Reusable (FAIR). This requires curatorial activities, which are expensive in terms of both time and effort. Based on our experience of supporting a multidisciplinary research team, we provide recommendations to direct the efforts of researchers towards affordable ways to achieve a reasonable degree of “FAIRness” for their data to become reusable upon its publication. The recommendations are accompanied by concrete insights on the challenges faced when trying to implement them in an actual data-intensive reference project.